Sorting Out Market Volatility’s Lessons

Asset price volatility is a critical variable for the various flavors of dynamic asset allocation and related strategies. As a core input for developing risk management models, ignoring volatility is like trying to drive with your eyes closed. As a first approximation for dealing with uncertainty in finance, volatility’s an obvious resource. But volatility can be confusing if you’re not thinking clearly about what “high” and “low” vol regimes imply for expected risk and return. It doesn’t help that the vast pool of research in this area dispenses recommendations that are all over the map. Fortunately, the basic lessons can be sorted out with minimal effort.

The first lesson is simply that volatility is volatile. High volatility eventually gives way to low volatility and vice versa. Timing is always in doubt, but the ebb and flow endures. In fact, one can argue that the cyclical aspect of volatility is the primary source of challenges and opportunities in finance. In any case, if you don’t embrace this fundamental feature of markets and all that it implies for risk management, well, you’re making a grave error. ‘Nuff said.

As a practical matter, the key issue with volatility is recognizing what it implies for expected return. This is where things get tricky. That’s due in part because volatility regimes are asymmetric, an empirical fact that can’t be emphasized too much. Whereas periods of high vol tend to be short and infrequent, it’s not unusual for low vol regimes to run on for months or even years.

As an example, consider the VIX Index, a popular measure of stock market volatility based on options prices. As the chart below reminds, volatility fluctuates, but it tends to be sticky at the low end. High vol, by contrast, is relatively brief and severe.

The value of monitoring and modeling volatility is directly linked to what the analysis implies for expected return. But the message can be confusing and even contradictory at first glance. For instance, think about what a low vol regime is telling us. A low VIX tends to be associated with bull markets and positive returns. We can take that one step further and say that a low VIX implies that future returns will be positive and relatively high. So far, so good. But since we also know that volatility is volatile, today’s run of low vol will eventually give way to high vol (and all the challenges that usually accompany such a dramatic change in the risk environment). As such, the arrival of low vol inspires predictions that a bull market’s demise is at hand. Maybe, but maybe not.

Recall that volatility regimes are also asymmetric in terms of duration. A period of low vol can and often does extend for much longer periods compared with high vol phases. This is a critical piece of information for interpreting a key finding in the finance literature: market volatility and market returns are negatively correlated. That’s true, of course, but putting that information to work, while recognizing the empirical facts above, requires a bit of careful thinking.

Let’s consider the current climate. Volatility is low and trailing market returns are high. On one level, the low vol regime that’s currently in play is predicting high returns. That’s a hard empirical fact, as any trader who practices position-sizing based on volatility will tell you. But how does this square with the reality that low vol leads to high vol? Shouldn’t we be anticipating low returns on the reasonable expectation that low vol will rise? Yes, but you can’t put the cart before the horse.

Anticipating low/negative returns while volatility is still low is premature until we see persuasive signals that the regime is changing. The issue here is not about rejecting the cyclical nature of volatility–that’s a given. Rather, the key point is that we should be monitoring volatility closely, deeply, in markets and for macro, for convincing clues that the low vol regime is giving way to its high vol counterpart.

Yes, easier said than done. Quite a lot of the problem is that our tools and techniques for measuring risk are flawed when it comes to looking ahead. But some flaws are worse than others. In any case, volatility is a critical piece of the risk management process. Yet measuring vol in a meaningful way so that we have relatively early and reliable signs of regime change is one of the tougher challenges in money management. There are some techniques that show promise—analyzing returns through a Hidden Markov Model filter, for instance.

Monitoring volatility with an eye on looking for convincing turning points is a worthwhile task, and there are many applications to consider. Quite a lot of the success (or failure) on this front is related to how you define volatility. The possibilities are wide open, ranging from standard deviation to average trading range to the VIX Index to value at risk (VaR), to name but a few. Each comes with its own set of pros and cons, which is to say there are no silver bullets. Accordingly, you can’t spend too much time researching this topic and backtesting the data. (In a future post I’ll take a closer look at some of the more promising methodologies for estimating vol.)

Even the best vol metric won’t help all that much if you don’t recognize the essential ebb and flow and all that it entails for anticipating risk levels. To summarize, volatility is negatively correlated with return. But the duration of vol regimes are also asymmetric. Recognizing that both sides of this risk coin tend to prevail tells us that we should be monitoring volatility regularly, with a model that has a decent chance of anticipating a regime change while there’s still time to react and boost the odds of productive results.

Yes, that’s a high standard, but it’s par for the course in finance, a realm that’s riddled with uncertainty. The good news is that modeling volatility is a gateway that helps us move from ignorance to enlightenment in matters of uncertainty. But the usual caveat applies, and so results will vary. Nonetheless, there’s no escaping reality. As Charles Kettering observed, “My interest is in the future because I am going to spend the rest of my life there.”